Detection of Cough Signals in Continuous Audio Recordings Using Hidden Markov Models [Електронний ресурс] / Sergio Matos, Surinder S. Birring, Ian D. Pavord, David H. Evans // IEEE Transactions on Biomedical Engineering [Електронний ресурс]. – 2006. – № 6. – Pp. 1078 – 1083
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Статистика використання: Завантажень: 1
Складова документа:
IEEE Transactions on Biomedical Engineering [Електронний ресурс] : вестник ин-та радиоинженеров. № 6. 53 / IEEE Engineering in medicine and Biology Group // IEEE Transactions on Biomedical Engineering. – USA, 2006
Анотація:
Cough is a common symptom of many respiratory diseases. The evaluation of its intensity and frequency of occurrence could provide valuable clinical information in the assessment
of patients with chronic cough. In this paper we propose the use of hidden Markov models (HMMs) to automatically detect cough sounds from continuous ambulatory recordings. The recording system consists of a digital sound recorder and a microphone attached to the patient’s chest. The recognition algorithm follows a keyword-spotting approach, with cough sounds representing the keywords. It was trained on 821 min selected from 10 ambulatory recordings, including 2473 manually labeled cough events, and tested on a database of nine recordings from separate patients with a total recording time of 3060 min and comprising 2155 cough events. The average detection rate was 82% at a false alarm
rate of seven events/h, when considering only events above an energy threshold relative to each recording’s average energy. These results sugge
of patients with chronic cough. In this paper we propose the use of hidden Markov models (HMMs) to automatically detect cough sounds from continuous ambulatory recordings. The recording system consists of a digital sound recorder and a microphone attached to the patient’s chest. The recognition algorithm follows a keyword-spotting approach, with cough sounds representing the keywords. It was trained on 821 min selected from 10 ambulatory recordings, including 2473 manually labeled cough events, and tested on a database of nine recordings from separate patients with a total recording time of 3060 min and comprising 2155 cough events. The average detection rate was 82% at a false alarm
rate of seven events/h, when considering only events above an energy threshold relative to each recording’s average energy. These results sugge